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mm_utils.py
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mm_utils.py
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
from PIL import Image
from io import BytesIO
import base64
import numpy as np
import os
import torch
from transformers import StoppingCriteria
from llava.constants import IMAGE_TOKEN_INDEX
import tempfile
from io import BytesIO
def get_frame_from_vcap(vidcap, num_frames=10, fps=None, frame_count=None):
import cv2
if fps == None or frame_count == None:
# if one of fps or frame_count is None, still recompute
fps = vidcap.get(cv2.CAP_PROP_FPS)
frame_count = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
if fps == 0 or frame_count == 0:
print("Video file not found. return empty images.")
return [
Image.new("RGB", (720, 720)),
] * num_frames
duration = frame_count / fps
frame_interval = frame_count // num_frames
if frame_interval == 0 and frame_count <= 1:
print("frame_interval is equal to 0. return empty image.")
return [
Image.new("RGB", (720, 720)),
] * num_frames
# print("duration:", duration, "frames:", frame_count, "intervals:", frame_interval)
images = []
count = 0
success = True
frame_indices = np.linspace(0, frame_count - 2, num_frames, dtype=int)
while success:
# print("frame_count:", frame_count, "count:", count, "num_frames:", num_frames, "frame_interval:", frame_interval)
if frame_count >= num_frames:
success, frame = vidcap.read()
if count in frame_indices:
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(img)
images.append(im_pil)
if len(images) >= num_frames:
return images
count += 1
else:
# Left padding frames if the video is not long enough
success, frame = vidcap.read()
if success:
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
im_pil = Image.fromarray(img)
images.append(im_pil)
count += 1
elif count >= 1:
width, height = images[-1].size
images = [Image.new("RGB", (width, height))] * (num_frames - len(images)) + images
print("padding frames:", (num_frames - len(images)))
return images
else:
break
raise ValueError("Did not find enough frames in the video. return empty image.")
def opencv_extract_frames(vpath_or_bytesio, frames=6, fps=None, frame_count=None):
"""
Extract frames from a video using OpenCV.
Args:
vpath_or_bytesio (str or BytesIO): Path to the video file or BytesIO object containing the video.
frames (int): Number of frames to extract from the video.
Returns:
list: List of PIL Images extracted from the video.
Raises:
NotImplementedError: If the type of `vpath_or_bytesio` is not supported.
"""
import cv2
if isinstance(vpath_or_bytesio, str):
vidcap = cv2.VideoCapture(vpath_or_bytesio)
return get_frame_from_vcap(vidcap, frames, fps=fps, frame_count=frame_count)
elif isinstance(vpath_or_bytesio, (BytesIO,)):
# assuming mp4
with tempfile.NamedTemporaryFile(delete=True, suffix=".mp4") as temp_video:
temp_video.write(vpath_or_bytesio.read())
temp_video_name = temp_video.name
vidcap = cv2.VideoCapture(temp_video_name)
return get_frame_from_vcap(vidcap, frames, fps=fps, frame_count=frame_count)
else:
raise NotImplementedError(type(vpath_or_bytesio))
def load_image_from_base64(image):
return Image.open(BytesIO(base64.b64decode(image)))
def expand2square(pil_img, background_color):
"""
Expand the given PIL image to a square shape by adding padding.
Parameters:
- pil_img: The PIL image to be expanded.
- background_color: The color of the padding to be added.
Returns:
- The expanded PIL image.
If the image is already square, it is returned as is.
If the image is wider than it is tall, padding is added to the top and bottom.
If the image is taller than it is wide, padding is added to the left and right.
"""
width, height = pil_img.size
if pil_img.mode == 'L':
background_color = background_color[0]
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def process_image(image_file, data_args, image_folder):
processor = data_args.image_processor
if isinstance(image_file, str):
if image_folder is not None:
image = Image.open(os.path.join(image_folder, image_file)).convert("RGB")
else:
image = Image.open(image_file).convert("RGB")
else:
# image is stored in bytearray
image = image_file
if data_args.image_aspect_ratio == "resize":
if hasattr(data_args.image_processor, "crop_size"):
# CLIP vision tower
crop_size = data_args.image_processor.crop_size
else:
# SIGLIP vision tower
assert hasattr(data_args.image_processor, "size")
crop_size = data_args.image_processor.size
image = image.resize((crop_size["height"], crop_size["width"]))
if data_args.image_aspect_ratio == "pad":
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(image, tuple(int(x * 255) for x in processor.image_mean))
image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
else:
# Using default behavior of the vision encoder
# For CLIP, default is central crop
# For Radio, default is central crop
# For Siglip, default is resize
# For InternVIT, default is resize
image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
return image
def process_images(images, image_processor, model_cfg):
model_cfg.image_processor = image_processor
new_images = [process_image(image, model_cfg, None) for image in images]
if all(x.shape == new_images[0].shape for x in new_images):
new_images = torch.stack(new_images, dim=0)
return new_images
def tokenizer_image_token(
prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None
):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("<image>")]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if (
len(prompt_chunks) > 0
and len(prompt_chunks[0]) > 0
and prompt_chunks[0][0] == tokenizer.bos_token_id
):
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == "pt":
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f"Unsupported tensor type: {return_tensors}")
return input_ids
def is_gemma_tokenizer(tokenizer):
return "gemma" in tokenizer.__class__.__name__.lower()
def get_model_name_from_path(model_path):
model_path = model_path.strip("/")
model_paths = model_path.split("/")
if model_paths[-1].startswith("checkpoint-"):
return model_paths[-2] + "_" + model_paths[-1]
else:
return model_paths[-1]
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.keyword_ids = []
self.max_keyword_len = 0
for keyword in keywords:
cur_keyword_ids = tokenizer(keyword).input_ids
if (
len(cur_keyword_ids) > 1
and cur_keyword_ids[0] == tokenizer.bos_token_id
):
cur_keyword_ids = cur_keyword_ids[1:]
if len(cur_keyword_ids) > self.max_keyword_len:
self.max_keyword_len = len(cur_keyword_ids)
self.keyword_ids.append(torch.tensor(cur_keyword_ids))
self.tokenizer = tokenizer
self.start_len = input_ids.shape[1]
def call_for_batch(
self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
) -> bool:
offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
self.keyword_ids = [
keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids
]
for keyword_id in self.keyword_ids:
if (output_ids[0, -keyword_id.shape[0] :] == keyword_id).all():
return True
outputs = self.tokenizer.batch_decode(
output_ids[:, -offset:], skip_special_tokens=True
)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False
def __call__(
self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
) -> bool:
outputs = []
for i in range(output_ids.shape[0]):
outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
return all(outputs)